Wu et al. [1]} provided long-term supportive information that enables video models to better understand the present. The designed a long-term feature bank and a feature bank operator FBO that computes interactions between the short-term and long-term features. They integrate information over a long temporal support, lasting minutes or even the whole video. Girdhar et al. [2]} proposed a transformer-style architecture to weight actors with features from the context around him. Tomei et al. [3]} employed self-attention to encode people and object relationships in a graph structure, and use the spatio-temporal distance between proposals. Ji et al. proposed Action Genome [4]} to model action-object interaction, by decomposing actions into spatio-temporal scene graphs. Ulutan et al. [5]} suggested combining actor features with every spatio-temporal region in the scene to produce attention maps between the actor and the context. Pan et al. [6]} proposed a relational reasoning module to capture the relation between the two actors based on their respective relations with the context. Tomei et al. [7]} proposed a graph-based framework to learn high-level interactions between people and objects, in both space and time. Spatio-temporal relationships are learned through self-attention on a multi-layer graph structure which can connect entities from consecutive clips, thus considering long-range spatial and temporal dependencies.
